Protein structural properties are diverse and have the characteristics of spatial hierarchy, such as secondary structures, solvent accessibility and backbone angles. Concurrent prediction of these tightly related structural features is more useful to understand the overall protein structure and functions. We proposed a multi-task deep learning method for concurrent prediction of protein secondary structures, solvent accessibility and backbone angles (phi,psi). The Supplementary online materials of the new method ( named CRRNN2 ) are provided here.
1.2 python 3
1.3 Keras 2.1.4 and tensorflow 1.13
1.4 blast 2.2.28 for preparing the PSSM feature set
1.5 HHsuite 3.0 for preparing the HHM feature set
Please follow the README in our software package in order to prepare input features and run our predictor. Script files in the software package is provided for demo how to run our model.
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